The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Bottom-up synthesis of large-scale graphene oxide nanosheets
Abstract
Graphene oxide nanosheets (GONs) have attracted considerable interest due to their potential applications in electronic and optoelectronic devices. Graphene oxide (GO) is usually prepared by using Hummers' method or modified Hummers' methods, which require graphite and strong oxidizers, followed by exfoliation to obtain GONs. The main drawbacks of this top-down approach are the use of strong oxidizing agents and small lateral size of GONs. Here, we present a self-assembly method to synthesize GONs with tunable thickness ranging from ∼1 nm (monolayer) to ∼1500 nm. The lateral sizes of the monolayer and few-layer (<5) GONs are about 20 μm and 100 μm respectively. The GONs are prepared by a hydrothermal method using glucose as a sole reagent. The method is environmentally friendly, facile, low-cost as well as capable of scaling up for mass production. The electrical, optical and structural properties of the as-grown and annealed GONs are comparable to GO and reduced GO prepared by the top-down method, respectively. The electrical resistivity of the GONs can be tuned by annealing for 8 orders of magnitude ranging from 106 Ω cm to 10−2 Ω cm. A GO-based photodetector has been fabricated, demonstrating the optoelectronic properties of the GONs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Two-dimensional gas of massless Dirac fermions in graphene
Novoselov, K. S., Geim, A. K., Morozov, S. V., Jiang, D., Katsnelson, M. I., Grigorieva, I. V., Dubonos, S. V., & Firsov, A. A. (2005) · Nature · doi:10.1038/nature04233 · Cited by 21,611
Superior Thermal Conductivity of Single-Layer Graphene
Balandin, A. A., Ghosh, S., Bao, W., Calizo, I., Teweldebrhan, D., Miao, F., & Lau, C. N. (2008) · Nano Letters · doi:10.1021/nl0731872 · Cited by 13,823
Reduction of graphene oxide via l -ascorbic acid
Zhang, J., Yang, H., Shen, G., Cheng, P., Zhang, J., & Guo, S. (2010) · Chem. Commun. · doi:10.1039/b917705a · Cited by 2,344
Universal optical conductance of graphite.
Kuzmenko, A. B., van Heumen, E., Carbone, F., & van der Marel, D. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.117401 · Cited by 1,026
Cited by
Smith, A. T., LaChance, A. M., Zeng, S., Liu, B., & Sun, L. (2019) · Nano Materials Science · doi:10.1016/j.nanoms.2019.02.004 · Cited by 1,643
Adsorption of VOCs onto engineered carbon materials: A review
Zhang, X., Gao, B., Creamer, A. E., Cao, C., & Li, Y. (2017) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2017.05.013 · Cited by 1,479
Brisebois, P. P., & Siaj, M. (2020) · Journal of Materials Chemistry C · doi:10.1039/c9tc03251g · Cited by 445
Graphene Oxide and Derivatives: The Place in Graphene Family
Dideikin, A. T., & Vul', A. Y. (2019) · Frontiers in Physics · doi:10.3389/fphy.2018.00149 · Cited by 444
Li, X., Lau, S. P., Tang, L., Ji, R., & Yang, P. (2014) · Nanoscale · doi:10.1039/c4nr00693c · Cited by 313
Self-citation
Metadata
- Authors
- Tang, L., Li, X., Ji, R., Teng, K. S., Tai, G., Ye, J., Wei, C., & Lau, S. P.
- Venue
- Journal of Materials Chemistry
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2012
- Volume
- 22
- Issue
- 12
- Pages
- 5676
- ISSN
- 0959-9428 (ISSN-L)
- 1364-5501
- OpenAlex
- W2118963551
- Cited by
- 291
- References
- 4
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bottom-up synthesis of large-scale graphene oxide nanosheets. https://app.cochat.ai/papers/10.1039/c2jm15944a
- The paper
- Tang, L., Li, X., Ji, R., Teng, K. S., Tai, G., Ye, J., Wei, C., & Lau, S. P. (2012). Bottom-up synthesis of large-scale graphene oxide nanosheets. Journal of Materials Chemistry. https://doi.org/10.1039/c2jm15944a
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.